节俭的在线激励机制为众包任务如实
Frugal Online Incentive Mechanisms for Crowdsourcing Tasks Truthfully
摘要：
移动人群感知（MCS）是一种新的模式，它利用无处不在的智能手机来有效地收集数据，从而实现许多新颖的应用。为了实现良好的服务质量的MCS应用，激励机制是必要的，以吸引更多的用户参与。大多数现有的机制只适用于离线的情况下，所有用户的信息是已知的先验。相反，我们关注的是一个更现实的场景，用户以随机顺序一个接一个地在线。基于在线拍卖模型，研究了用户在到达时向众包者提交个人信息的问题，众包者的目标是在指定的截止日期之前选择一部分用户，使总支付最小，同时完成指定数量的任务，设计了三种在线机制：Homo-OMZ，Hetero-OMZ和Hetero-OMG，它们都能满足计算效率，个人理性、成本真实性和消费者主权。Homo-OMZ机制适用于同质用户模型，能够满足社会效率但不满足恒节俭。Hetero-OMZ和Hetero-OMG机制适用于同构和异构用户模型，并能满足恒定的节俭性。此外，Hetero-OMG机制还可以满足时间真实性。通过大量的模拟，我们评估的性能和验证我们的在线机制的理论属性。
英文摘要：
Mobile Crowd Sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private profiles to the crowdsourcer when they arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for minimizing the total payment while a specific number of tasks can be completed.We design three online mechanisms, Homo-OMZ, Hetero-OMZ and Hetero-OMG, all of which can satisfy the computational efficiency, individual rationality, cost-truthfulness, and consumer sovereignty. The Homo-OMZ mechanism is applicable to the homogeneous user model and can satisfy the social efficiency but not constant frugality. The Hetero-OMZ and Hetero-OMG mechanisms are applicable to both the homogeneous and heterogeneous user models, and can satisfy the constant frugality. Besides, the Hetero-OMG mechanism can also satisfy the time-truthfulness. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
